Analysis of Off-Road Tire Cornering Characteristics by Using Advanced Analytical Techniques
Bibliographic record
Abstract
ABSTRACT This paper focuses on analysis of the cornering characteristics of an off-road truck tire running under several operating conditions over different soils. The finite element analysis (FEA) method is used to model the Goodyear RHD 315/80R22.5 truck tire, and the smoothed-particle hydrodynamics (SPH) method is used to model the soil. The goal of this research is to provide a virtual testing environment in Pam-Crash software as an alternative to actual tests for FEA and SPH analyses of rolling tire interactions on deformable terrains. The study on the effects of different operating parameters on the cornering performance combined with the sensitivity study can be of interest to tire engineers or vehicle engineers because they provide insight into the design and real-time behavior of a vehicle. Tire and soil models are validated using experimental data and published measurements, showing good agreement. The tire–soil interaction is investigated under different tire conditions, such as longitudinal speed, inflation pressure, vertical load, and slip angle, and under various soil characteristics, such as cohesion, internal friction angle, and rut depth. Cornering force, self-aligning moment, and overturning moment are studied as the fundamental cornering characteristics that affect truck lateral stability and control. Owing to the excessive computational demands posed by the FEA-SPH tire–soil models, we propose unique mathematical relationships for estimating the cornering characteristics of free-rolling as well as driven truck tires in an efficient manner. The genetic algorithm (GA) technique is used to develop relationships between the cornering parameters and operating conditions. We conclude that the identified mathematical relationships could provide very good estimations of the cornering characteristics under a broad range of operating conditions and soils. The GA equations will ultimately be implemented into a full vehicle model to evaluate the full vehicle performance.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".